Nationalism in the Age of Brexit: The Attitudes and Identities of Young Voters
Bibliographic record
Abstract
The 2016 Brexit referendum revealed a division between younger voters, a majority of whom voted Remain, and older voters, a majority of whom voted Leave. From virtual interviews with six British young adults, this article analyzes the effects of the Brexit referendum on their perceptions of belonging and national identity. My theoretical framework draws upon Benedict Anderson’s definition of the nation and Michael Skey’s and Craig Calhoun’s critique that feelings of equality among members are unrealistic due to the power and identity hierarchies that exist within a nation. Interviews reveal a strong binary conception of identities created through politics and media that divide voters into distinct, distanced groups. Young voters use harsh, derogatory language to describe oppositional groups, such as Conservatives, Leave voters, and older voters, to separate themselves and reinforce their identities. However, because these oppositional groups hold the most power, continuous separation reinforces feelings of powerlessness in politics and reveals hierarchies of identities. These hierarchies can have long-lasting implications for the United Kingdom as these younger voters will eventually comprise the voting majority and strive to see their values and beliefs represented in positions of power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".